Proof pending. Core topic summary fields are still materializing.
Recent advancements in sentiment analysis are increasingly focused on addressing the complexities of multilingual and multi-dimensional sentiment detection, particularly in diverse contexts like social media and informal communication. Research on code-mixed languages, such as Hinglish, is enhancing brand monitoring capabilities by developing models that effectively interpret the linguistic nuances of hybrid languages. Simultaneously, new methodologies for multi-valence sentiment analysis are emerging, allowing for the identification of both positive and negative sentiments within the same message, which is crucial for understanding public discourse in political and social contexts. Additionally, the integration of large language models with traditional NLP techniques is proving effective in dimensional aspect-based sentiment analysis, improving prediction stability and accuracy. These developments not only promise to refine sentiment tracking for businesses but also highlight the need for models that can navigate the intricacies of human expression across varied platforms and languages, addressing the challenges of sentiment polarization and neutrality in AI outputs.
The effectiveness of brand monitoring in India is increasingly challenged by the rise of Hinglish--a hybrid of Hindi and English--used widely in user-generated content on platforms like Twitter. Tradi...
The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, compassion) and anti-social sentiment (e.g...
We present our system for SemEval-2026 Task 3 on dimensional aspect-based sentiment regression. Our approach combines a hybrid RoBERTa encoder, which jointly predicts sentiment using regression and di...
This paper describes LogSigma, our system for SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA). Unlike traditional Aspect-Based Sentiment Analysis (ABSA), which predicts disc...
Point-level weakly-supervised temporal sentiment localization (P-WTSL) aims to detect sentiment-relevant segments in untrimmed multimodal videos using timestamp sentiment annotations, which greatly re...
Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehe...
Aspect-Based Sentiment Intensity Analysis (ABSIA) has garnered increasing attention, though research largely focuses on domain-specific, sentence-level settings. In contrast, document-level ABSIA--par...
The use of Transfer Learning & Transformers has steadily improved accuracy and has significantly contributed in solving complex computation problems. However, this transformer led accuracy improvement...
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Canonical route: /topics
Agent Handoff
Canonical ID sentiment-analysis | Route /topic/sentiment-analysis
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/sentiment-analysisMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Sentiment Analysis",
"cluster": "Sentiment Analysis"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Sentiment Analysis",
"normalized_query": "sentiment-analysis",
"route": "/topic/sentiment-analysis",
"paper_ref": null,
"topic_slug": "sentiment-analysis",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.